Article excerpt

Organisms do not always respond in the same way to the same stimuli. Typically, this stochastic nature of psychophysical responses is modelled as a Gaussian process. This is more a matter of convenience than anything else. Gaussian statistics are relatively easy to work with, and in most cases it is only the variance of internal noise that matters, not the shape of its distribution. On the other hand, there is growing interest in models of behavior on individual trials, and likelihood-based fits of these models do require more a more precise characterization of the statistics of sensory noise.

Neri (2013) proves the noise that limits masked pattern detection has excess kurtosis. You might say its distribution is peaky. To appreciate Neri's proof, it helps if you first understand that most psychometric functions are completely uninformative regarding the distribution of internal noise. When deciding how to respond in a psychophysical experiment, observers evaluate sensory evidence on an internal scale (some call it the decision axis), which need not be a linear transformation of the signal intensity over which psychometric functions are typically drawn. Neri used the aforementioned analysis of individual trials to infer the transformation from stimulus space to the decision axis. …